Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations17379
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory195.0 B

Variable types

Numeric11
DateTime1
Categorical5

Alerts

atemp is highly overall correlated with casual and 2 other fieldsHigh correlation
casual is highly overall correlated with atemp and 3 other fieldsHigh correlation
cnt is highly overall correlated with casual and 2 other fieldsHigh correlation
hr is highly overall correlated with cnt and 1 other fieldsHigh correlation
instant is highly overall correlated with season and 1 other fieldsHigh correlation
mnth is highly overall correlated with seasonHigh correlation
registered is highly overall correlated with casual and 2 other fieldsHigh correlation
season is highly overall correlated with atemp and 3 other fieldsHigh correlation
temp is highly overall correlated with atemp and 2 other fieldsHigh correlation
weekday is highly overall correlated with workingdayHigh correlation
workingday is highly overall correlated with weekdayHigh correlation
yr is highly overall correlated with instantHigh correlation
holiday is highly imbalanced (81.2%)Imbalance
instant is uniformly distributedUniform
instant has unique valuesUnique
hr has 726 (4.2%) zerosZeros
weekday has 2502 (14.4%) zerosZeros
windspeed has 2180 (12.5%) zerosZeros
casual has 1581 (9.1%) zerosZeros

Reproduction

Analysis started2025-11-10 06:30:32.243995
Analysis finished2025-11-10 06:30:38.845001
Duration6.6 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

instant
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct17379
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8690
Minimum1
Maximum17379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:38.917000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile869.9
Q14345.5
median8690
Q313034.5
95-th percentile16510.1
Maximum17379
Range17378
Interquartile range (IQR)8689

Descriptive statistics

Standard deviation5017.0295
Coefficient of variation (CV)0.57733366
Kurtosis-1.2
Mean8690
Median Absolute Deviation (MAD)4345
Skewness0
Sum1.5102351 × 108
Variance25170585
MonotonicityStrictly increasing
2025-11-10T12:00:38.979001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
115921
 
< 0.1%
115781
 
< 0.1%
115791
 
< 0.1%
115801
 
< 0.1%
115811
 
< 0.1%
115821
 
< 0.1%
115831
 
< 0.1%
115841
 
< 0.1%
115851
 
< 0.1%
Other values (17369)17369
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
173791
< 0.1%
173781
< 0.1%
173771
< 0.1%
173761
< 0.1%
173751
< 0.1%
173741
< 0.1%
173731
< 0.1%
173721
< 0.1%
173711
< 0.1%
173701
< 0.1%

dteday
Date

Distinct731
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
Minimum2011-01-01 00:00:00
Maximum2012-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-10T12:00:39.042999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:39.113430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

season
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.5 KiB
3
4496 
2
4409 
1
4242 
4
4232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Length

2025-11-10T12:00:39.180747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T12:00:39.217745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring characters

ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
34496
25.9%
24409
25.4%
14242
24.4%
44232
24.4%

yr
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.5 KiB
1
8734 
0
8645 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Length

2025-11-10T12:00:39.265746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T12:00:39.299745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring characters

ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18734
50.3%
08645
49.7%

mnth
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5377755
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:39.333745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4387757
Coefficient of variation (CV)0.52598559
Kurtosis-1.2018782
Mean6.5377755
Median Absolute Deviation (MAD)3
Skewness-0.0092532484
Sum113620
Variance11.825178
MonotonicityNot monotonic
2025-11-10T12:00:39.381503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
51488
8.6%
71488
8.6%
121483
8.5%
81475
8.5%
31473
8.5%
101451
8.3%
61440
8.3%
41437
8.3%
91437
8.3%
111437
8.3%
Other values (2)2770
15.9%
ValueCountFrequency (%)
11429
8.2%
21341
7.7%
31473
8.5%
41437
8.3%
51488
8.6%
61440
8.3%
71488
8.6%
81475
8.5%
91437
8.3%
101451
8.3%
ValueCountFrequency (%)
121483
8.5%
111437
8.3%
101451
8.3%
91437
8.3%
81475
8.5%
71488
8.6%
61440
8.3%
51488
8.6%
41437
8.3%
31473
8.5%

hr
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.546752
Minimum0
Maximum23
Zeros726
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:39.426900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9144051
Coefficient of variation (CV)0.5988182
Kurtosis-1.1980206
Mean11.546752
Median Absolute Deviation (MAD)6
Skewness-0.01067991
Sum200671
Variance47.808998
MonotonicityNot monotonic
2025-11-10T12:00:39.475475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17730
 
4.2%
16730
 
4.2%
13729
 
4.2%
15729
 
4.2%
14729
 
4.2%
12728
 
4.2%
22728
 
4.2%
21728
 
4.2%
20728
 
4.2%
19728
 
4.2%
Other values (14)10092
58.1%
ValueCountFrequency (%)
0726
4.2%
1724
4.2%
2715
4.1%
3697
4.0%
4697
4.0%
5717
4.1%
6725
4.2%
7727
4.2%
8727
4.2%
9727
4.2%
ValueCountFrequency (%)
23728
4.2%
22728
4.2%
21728
4.2%
20728
4.2%
19728
4.2%
18728
4.2%
17730
4.2%
16730
4.2%
15729
4.2%
14729
4.2%

holiday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.5 KiB
0
16879 
1
 
500

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Length

2025-11-10T12:00:39.527519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T12:00:39.773202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring characters

ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016879
97.1%
1500
 
2.9%

weekday
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0036826
Minimum0
Maximum6
Zeros2502
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:39.798208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0057715
Coefficient of variation (CV)0.66777077
Kurtosis-1.2559969
Mean3.0036826
Median Absolute Deviation (MAD)2
Skewness-0.0029982214
Sum52201
Variance4.0231191
MonotonicityNot monotonic
2025-11-10T12:00:39.838170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
62512
14.5%
02502
14.4%
52487
14.3%
12479
14.3%
32475
14.2%
42471
14.2%
22453
14.1%
ValueCountFrequency (%)
02502
14.4%
12479
14.3%
22453
14.1%
32475
14.2%
42471
14.2%
52487
14.3%
62512
14.5%
ValueCountFrequency (%)
62512
14.5%
52487
14.3%
42471
14.2%
32475
14.2%
22453
14.1%
12479
14.3%
02502
14.4%

workingday
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.5 KiB
1
11865 
0
5514 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Length

2025-11-10T12:00:39.888201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T12:00:39.920205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring characters

ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
111865
68.3%
05514
31.7%

weathersit
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.5 KiB
1
11413 
2
4544 
3
1419 
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Length

2025-11-10T12:00:39.962212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T12:00:39.999205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
111413
65.7%
24544
 
26.1%
31419
 
8.2%
43
 
< 0.1%

temp
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49698717
Minimum0.02
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:40.064275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.2
Q10.34
median0.5
Q30.66
95-th percentile0.8
Maximum1
Range0.98
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.19255612
Coefficient of variation (CV)0.38744687
Kurtosis-0.9418442
Mean0.49698717
Median Absolute Deviation (MAD)0.16
Skewness-0.0060208833
Sum8637.14
Variance0.03707786
MonotonicityNot monotonic
2025-11-10T12:00:40.131637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62726
 
4.2%
0.66693
 
4.0%
0.64692
 
4.0%
0.7690
 
4.0%
0.6675
 
3.9%
0.36671
 
3.9%
0.34645
 
3.7%
0.3641
 
3.7%
0.4614
 
3.5%
0.32611
 
3.5%
Other values (40)10721
61.7%
ValueCountFrequency (%)
0.0217
 
0.1%
0.0416
 
0.1%
0.0616
 
0.1%
0.0817
 
0.1%
0.151
 
0.3%
0.1276
 
0.4%
0.14138
 
0.8%
0.16230
1.3%
0.18155
0.9%
0.2354
2.0%
ValueCountFrequency (%)
11
 
< 0.1%
0.981
 
< 0.1%
0.9616
 
0.1%
0.9417
 
0.1%
0.9249
 
0.3%
0.990
0.5%
0.8853
 
0.3%
0.86131
0.8%
0.84138
0.8%
0.82213
1.2%

atemp
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4757751
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:40.195657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2121
Q10.3333
median0.4848
Q30.6212
95-th percentile0.7424
Maximum1
Range1
Interquartile range (IQR)0.2879

Descriptive statistics

Standard deviation0.17185022
Coefficient of variation (CV)0.36120052
Kurtosis-0.84541189
Mean0.4757751
Median Absolute Deviation (MAD)0.1364
Skewness-0.090428859
Sum8268.4955
Variance0.029532497
MonotonicityNot monotonic
2025-11-10T12:00:40.259741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6212988
 
5.7%
0.5152618
 
3.6%
0.4091614
 
3.5%
0.3333600
 
3.5%
0.6667593
 
3.4%
0.6061588
 
3.4%
0.5303579
 
3.3%
0.5575
 
3.3%
0.4545559
 
3.2%
0.303549
 
3.2%
Other values (55)11116
64.0%
ValueCountFrequency (%)
02
 
< 0.1%
0.01524
 
< 0.1%
0.03038
 
< 0.1%
0.04559
 
0.1%
0.060614
 
0.1%
0.075828
 
0.2%
0.090913
 
0.1%
0.106135
 
0.2%
0.121286
0.5%
0.136490
0.5%
ValueCountFrequency (%)
11
 
< 0.1%
0.98482
 
< 0.1%
0.95451
 
< 0.1%
0.92425
 
< 0.1%
0.90915
 
< 0.1%
0.893915
 
0.1%
0.878819
0.1%
0.863620
0.1%
0.848532
0.2%
0.833341
0.2%

hum
Real number (ℝ)

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62722884
Minimum0
Maximum1
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:40.327188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31
Q10.48
median0.63
Q30.78
95-th percentile0.93
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.19292983
Coefficient of variation (CV)0.30759082
Kurtosis-0.82611674
Mean0.62722884
Median Absolute Deviation (MAD)0.15
Skewness-0.11128715
Sum10900.61
Variance0.037221921
MonotonicityNot monotonic
2025-11-10T12:00:40.392725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88657
 
3.8%
0.83630
 
3.6%
0.94560
 
3.2%
0.87488
 
2.8%
0.7430
 
2.5%
0.66388
 
2.2%
0.65387
 
2.2%
0.69359
 
2.1%
0.55352
 
2.0%
0.74341
 
2.0%
Other values (79)12787
73.6%
ValueCountFrequency (%)
022
0.1%
0.081
 
< 0.1%
0.11
 
< 0.1%
0.121
 
< 0.1%
0.131
 
< 0.1%
0.142
 
< 0.1%
0.154
 
< 0.1%
0.1610
0.1%
0.1710
0.1%
0.1810
0.1%
ValueCountFrequency (%)
1270
1.6%
0.971
 
< 0.1%
0.963
 
< 0.1%
0.94560
3.2%
0.93331
1.9%
0.922
 
< 0.1%
0.911
 
< 0.1%
0.97
 
< 0.1%
0.89239
 
1.4%
0.88657
3.8%

windspeed
Real number (ℝ)

Zeros 

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19009761
Minimum0
Maximum0.8507
Zeros2180
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:40.448979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1045
median0.194
Q30.2537
95-th percentile0.4179
Maximum0.8507
Range0.8507
Interquartile range (IQR)0.1492

Descriptive statistics

Standard deviation0.12234023
Coefficient of variation (CV)0.64356533
Kurtosis0.59082041
Mean0.19009761
Median Absolute Deviation (MAD)0.0895
Skewness0.5749052
Sum3303.7063
Variance0.014967132
MonotonicityNot monotonic
2025-11-10T12:00:40.498978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
02180
12.5%
0.13431738
10.0%
0.16421695
9.8%
0.1941657
9.5%
0.10451617
9.3%
0.22391513
8.7%
0.08961425
8.2%
0.25371295
7.5%
0.28361048
6.0%
0.2985808
 
4.6%
Other values (20)2403
13.8%
ValueCountFrequency (%)
02180
12.5%
0.08961425
8.2%
0.10451617
9.3%
0.13431738
10.0%
0.16421695
9.8%
0.1941657
9.5%
0.22391513
8.7%
0.25371295
7.5%
0.28361048
6.0%
0.2985808
 
4.6%
ValueCountFrequency (%)
0.85072
 
< 0.1%
0.83581
 
< 0.1%
0.8062
 
< 0.1%
0.77611
 
< 0.1%
0.74632
 
< 0.1%
0.71642
 
< 0.1%
0.68665
 
< 0.1%
0.656711
0.1%
0.641814
0.1%
0.611923
0.1%

casual
Real number (ℝ)

High correlation  Zeros 

Distinct322
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.676218
Minimum0
Maximum367
Zeros1581
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:40.553978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median17
Q348
95-th percentile138.1
Maximum367
Range367
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.30503
Coefficient of variation (CV)1.3820139
Kurtosis7.5710017
Mean35.676218
Median Absolute Deviation (MAD)16
Skewness2.4992369
Sum620017
Variance2430.986
MonotonicityNot monotonic
2025-11-10T12:00:40.618979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01581
 
9.1%
11082
 
6.2%
2798
 
4.6%
3697
 
4.0%
4561
 
3.2%
5509
 
2.9%
6448
 
2.6%
7405
 
2.3%
8377
 
2.2%
9348
 
2.0%
Other values (312)10573
60.8%
ValueCountFrequency (%)
01581
9.1%
11082
6.2%
2798
4.6%
3697
4.0%
4561
 
3.2%
5509
 
2.9%
6448
 
2.6%
7405
 
2.3%
8377
 
2.2%
9348
 
2.0%
ValueCountFrequency (%)
3671
< 0.1%
3621
< 0.1%
3611
< 0.1%
3571
< 0.1%
3561
< 0.1%
3551
< 0.1%
3541
< 0.1%
3521
< 0.1%
3501
< 0.1%
3471
< 0.1%

registered
Real number (ℝ)

High correlation 

Distinct776
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.78687
Minimum0
Maximum886
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:40.686436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q134
median115
Q3220
95-th percentile465
Maximum886
Range886
Interquartile range (IQR)186

Descriptive statistics

Standard deviation151.35729
Coefficient of variation (CV)0.98420162
Kurtosis2.7500178
Mean153.78687
Median Absolute Deviation (MAD)89
Skewness1.5579042
Sum2672662
Variance22909.028
MonotonicityNot monotonic
2025-11-10T12:00:40.750433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307
 
1.8%
3294
 
1.7%
5287
 
1.7%
6266
 
1.5%
2245
 
1.4%
1201
 
1.2%
7200
 
1.2%
8190
 
1.1%
9178
 
1.0%
11140
 
0.8%
Other values (766)15071
86.7%
ValueCountFrequency (%)
024
 
0.1%
1201
1.2%
2245
1.4%
3294
1.7%
4307
1.8%
5287
1.7%
6266
1.5%
7200
1.2%
8190
1.1%
9178
1.0%
ValueCountFrequency (%)
8861
< 0.1%
8851
< 0.1%
8762
< 0.1%
8711
< 0.1%
8601
< 0.1%
8572
< 0.1%
8391
< 0.1%
8381
< 0.1%
8331
< 0.1%
8221
< 0.1%

cnt
Real number (ℝ)

High correlation 

Distinct869
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.46309
Minimum1
Maximum977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2025-11-10T12:00:40.815433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q140
median142
Q3281
95-th percentile563.1
Maximum977
Range976
Interquartile range (IQR)241

Descriptive statistics

Standard deviation181.3876
Coefficient of variation (CV)0.95737698
Kurtosis1.4172033
Mean189.46309
Median Absolute Deviation (MAD)112
Skewness1.2774116
Sum3292679
Variance32901.461
MonotonicityNot monotonic
2025-11-10T12:00:40.879439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5260
 
1.5%
6236
 
1.4%
4231
 
1.3%
3224
 
1.3%
2208
 
1.2%
7198
 
1.1%
8182
 
1.0%
1158
 
0.9%
10155
 
0.9%
11147
 
0.8%
Other values (859)15380
88.5%
ValueCountFrequency (%)
1158
0.9%
2208
1.2%
3224
1.3%
4231
1.3%
5260
1.5%
6236
1.4%
7198
1.1%
8182
1.0%
9128
0.7%
10155
0.9%
ValueCountFrequency (%)
9771
< 0.1%
9761
< 0.1%
9701
< 0.1%
9681
< 0.1%
9671
< 0.1%
9631
< 0.1%
9571
< 0.1%
9531
< 0.1%
9481
< 0.1%
9431
< 0.1%

Interactions

2025-11-10T12:00:38.131320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:32.769156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.308217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.786051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.412150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.901546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.422864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.922658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.415736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.106157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.654328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.191765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:32.820200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.355218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.831057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.460765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.951291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.471391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.970742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.642087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.171881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.699774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.247058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:32.874102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.398227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.872051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.504459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.998417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.516391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.014745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.685665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.223576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.742252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.294997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:32.921464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.438226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.911051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.547188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.066726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.560391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.058092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.728905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.269597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.785094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.339419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:32.968223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.483227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.104458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.590646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.111265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.604127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.102950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.772300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.315544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.826630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.382259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.019732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.524225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.147458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.635644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.153075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.657469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.146541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.816676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.362700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.868637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.426713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.063734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.567220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.192458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.680662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.198328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.703380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.190540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.858676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.411613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.910632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.477738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.111219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.611419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.234751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.723410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.243224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.746444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.236542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.901377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.471624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.955584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.532504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.159226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.653417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.278576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.767411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.286212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.788867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.280541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.941499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.515424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.000457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.589502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.208220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.699027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.321132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.815208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.334797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.836664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.328086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.995630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.560830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.046319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.631582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.254218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:33.743916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.365134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:34.857201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.378284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:35.879706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:36.370761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.051160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:37.608972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T12:00:38.087320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-10T12:00:40.933434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
atempcasualcntholidayhrhuminstantmnthregisteredseasontempweathersitweekdaywindspeedworkingdayyr
atemp1.0000.5700.4230.0520.133-0.0530.1260.1910.3730.5080.9900.115-0.013-0.0410.0750.082
casual0.5701.0000.8510.0580.477-0.3880.1590.1180.7810.1970.5710.0910.0130.1230.3460.162
cnt0.4230.8511.0000.0420.511-0.3600.2440.1260.9890.1610.4230.0930.0300.1270.1210.265
holiday0.0520.0580.0421.0000.0000.0390.0610.1030.0510.0430.0520.0200.2840.0000.2520.000
hr0.1330.4770.5110.0001.000-0.279-0.005-0.0060.5110.0000.1340.050-0.0030.1400.0000.000
hum-0.053-0.388-0.3600.039-0.2791.0000.0070.160-0.3380.134-0.0550.286-0.037-0.2940.0500.127
instant0.1260.1590.2440.061-0.0050.0071.0000.4890.2560.8110.1280.0890.001-0.0730.0290.995
mnth0.1910.1180.1260.103-0.0060.1600.4891.0000.1270.8880.1910.0780.010-0.1300.0600.000
registered0.3730.7810.9890.0510.511-0.3380.2560.1271.0000.1480.3730.0850.0350.1230.1710.273
season0.5080.1970.1610.0430.0000.1340.8110.8880.1481.0000.5250.0550.0000.1050.0320.000
temp0.9900.5710.4230.0520.134-0.0550.1280.1910.3730.5251.0000.095-0.006-0.0100.0810.090
weathersit0.1150.0910.0930.0200.0500.2860.0890.0780.0850.0550.0951.0000.0430.0500.0430.030
weekday-0.0130.0130.0300.284-0.003-0.0370.0010.0100.0350.000-0.0060.0431.0000.0100.9390.000
windspeed-0.0410.1230.1270.0000.140-0.294-0.073-0.1300.1230.105-0.0100.0500.0101.0000.0000.022
workingday0.0750.3460.1210.2520.0000.0500.0290.0600.1710.0320.0810.0430.9390.0001.0000.000
yr0.0820.1620.2650.0000.0000.1270.9950.0000.2730.0000.0900.0300.0000.0220.0001.000

Missing values

2025-11-10T12:00:38.703535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-10T12:00:38.790991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-01101006010.240.28790.810.000031316
122011-01-01101106010.220.27270.800.000083240
232011-01-01101206010.220.27270.800.000052732
342011-01-01101306010.240.28790.750.000031013
452011-01-01101406010.240.28790.750.0000011
562011-01-01101506020.240.25760.750.0896011
672011-01-01101606010.220.27270.800.0000202
782011-01-01101706010.200.25760.860.0000123
892011-01-01101806010.240.28790.750.0000178
9102011-01-01101906010.320.34850.760.00008614
instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
17369173702012-12-3111121401120.280.27270.450.223962185247
17370173712012-12-3111121501120.280.28790.450.134369246315
17371173722012-12-3111121601120.260.25760.480.194030184214
17372173732012-12-3111121701120.260.28790.480.089614150164
17373173742012-12-3111121801120.260.27270.480.134310112122
17374173752012-12-3111121901120.260.25760.600.164211108119
17375173762012-12-3111122001120.260.25760.600.164288189
17376173772012-12-3111122101110.260.25760.600.164278390
17377173782012-12-3111122201110.260.27270.560.1343134861
17378173792012-12-3111122301110.260.27270.650.1343123749